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DTSTART:20191108T110000Z
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CREATED:20191104
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SUMMARY:Data Processing Methodologies in the Area of E-Health for Categorizing Therapeutic Responses in Patients with Migraine (Seminar)
DESCRIPTION:Franklin Parrales Bravo,\n\n Complutense University of Madrid (UCM)\nAbstract:\nMigraine is a chronic disease that affects the daily development of\nactivities of people around the world. To alleviate the symptoms,\nOnabotulinumtoxinA (BoNT-A) has solid proven evidence for their use\naccording to various works and clinical trials. Nowadays, it is known\nthat 70-80% of patients with chronic migraine show an improvement with\nthis treatment (improvement defined as a reduction in migraine attack\nfrequency or days with attacks by at least 50% within 3 months,\nleading to a significantly improved functioning of the patients and\ntheir overall quality of life). As has been mentioned by [1], it is\nvery important to predict if the BoNT-A treatment will be effective in\na patient. Knowing the phenotype-response relationship may help in the\ndevelopment of new treatments for the 20-30% of patients that do not\nrespond to the treatment.\nThis talk will describe two approaches for addressing the prediction\nof the therapeutic response to BoNT-A: panoramic and feedback\nprediction [2].\nPanoramic prediction makes it possible to decide whether the treatment\nwill be beneficial without using previous knowledge and without\ninvolving unnecessary treatments. Feedback prediction can be more\naccurate prediction since it considers the results of previous stages\nof the treatment. With the purpose of unveiling the medical attributes\nthat make treatment effective for patients, consensus models are\napplied to the prediction models found through the proposed\napproaches. The following attributes have been found to be relevant\nwhen predicting the treatment response to BoNT-A:\nmigraine time evolution, unilateral pain, analgesic abuse, headache\ndays and the retroocular component. According to doctors, these\nfactors are also medically relevant and in alignment with the medical\nliterature.\nWhen training the prediction models, an attribute weighting task is\nconsidered. It is performed with the purpose of finding those weights\nthat improve the representation of the numeric labels encoded by\ndoctors for each stage of BoNT-A treatment. In the panoramic\nprediction, the attribute weighting is multiobjective because we need\nto find the optimal weights that improve the prediction accuracy for\nall stages, simultaneously. In this sense, multiobjective evolutionary\nalgorithms (MOEAs) that support parallelization have been considered\nfor improving the training time of predictive models [3].\nThe obtained results show accuracies close to 85% and 90% for\npanoramic and feedback prediction approaches, respectively. Moreover,\nthe training time of the panoramic prediction models is decreased from\n8 to less than 2 hours when using 8 threads.\nBio\nFRANKLIN PARRALES BRAVO received the M.Sc. degree in computer science from the Complutense University of Madrid (UCM), Spain, in 2015, where he received a scholarship to develop the master’s Thesis with the Department of Computer Architecture and Automation (DACYA-UCM) by the Ecuadorian Ministry of Education, Science, Technology and Innovation (SENESCYT) under the 165-ARG5-2013 grant, and the M.Sc. degree in artificial intelligence from the Technical University of Madrid (UPM), Spain, in 2019. He has been granted a predoctoral fellowship by SENESCYT-Ecuador under the 8905- AR5G-2016 grant, to develop his Ph.D. thesis at UCM. Since 2016, he has been an Associate Professor of computer science with the University of Guayaquil, Ecuador, where he is currently focused on data processing methodologies in e-Health for categorizing therapeutic responses in patients with migraine. He is currently with the Department of Computer Architecture and System Engineering, Complutense University of Madrid, Madrid, Spain. His current research interests include e-Health and machine learning.\nFor more information:\n\nlmsrusso@gmail.com ( mailto:lmsrusso@gmail.com )\n\n\n
URL:https://www.inesc-id.pt/events/data-processing-methodologies-in-the-area-of-e-health-for-categorizing-therapeutic-responses-in-patients-with-migraine-seminar/
CATEGORIES:Seminars & Lectures
LOCATION:INESC-ID Lisboa
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